In this paper, we propose the design, operation, and implementation of an Internet of Things based hybrid structural health monitoring. This innovative system leverages the capabilities of both fog and cloud layers in computing and monitoring. The system architecture consists of leaf nodes deployed on a target structure. These nodes, synchronously, collect acceleration signals from accelerometers attached directly to the structure and transmit the data to an on-site central node via short-range communication protocol. At the fog layer, the central node, applies damage detection algorithms on the collected data. If a damage is detected, it forwards the acceleration signals to a cloud-based monitoring server using cellular internet connectivity where more complex algorithms are used to identify and locate the damage. We provide detailed information about the design of the different system nodes, the implementation of damage detection algorithms, and the architecture of the monitoring server. To evaluate the effectiveness of the proposed system, several practical experiments were conducted. The results demonstrate that the hybrid system presented in this paper provides an efficient, reliable and cost-effective approach to damage detection and identification in civil infrastructures.

In this paper, we present an Internet of Things framework for structural health monitoring. The proposed system detects the delimitation and debonding in composite concrete structures: more specifically, develop an IoT-Based Non-Destructive Test (NDT) to detect debonding between Carbon Fiber sheets and Concrete slabs. An Inter Digital Capacitance Sensor is designed and fabricated to function as the primary detection element. The sensor is embedded within an IoT node that manages the measurement process, captures the measurement location automatically, and performs essential data filtering operations with an attached graphical interface that allows basic control and early-access to the measured data. The IoT node integrates within the larger framework via wireless WiFi connection throughout which the data is transferred and control functions are administrated. The paper presents the system design details including mathematical modeling for the capacitance sensing element, finite element simulation results, and practical setup measured data.

This study explores the role of green building technologies in the development of a sustainable future by evaluating the techno‐economic benefits of phase change materials (PCMs) in building walls. The purpose of this research is to investigate how PCMs can be incorporated into bricks to reduce the use of electricity for cooling the building and temperature fluctuations caused by varying indoor temperatures ( T _i = 18, 20, 22, and 24°C) and outdoor temperatures ( T _o = 25, 28, 31, and 34°C). An analysis of the thermal and economic behavior of three aluminum cylindrical capsules containing PCM embedded within brick structures is presented using computational fluid dynamics (CFDs) simulations conducted over a period of 12 h in order to determine the interaction between the brick, the PCM capsules, and the buoyant forces. Results indicate that PCM capsules perform optimally within specific temperature ranges, achieving their peak efficacy between 20 and 24°C, while 25–31°C are ideal T _o values. It is estimated that adoption of PCM‐integrated bricks can save you between $414.7k and $444.3k over the course of 30 years. The internal rate of return (IRR) of this system averages 1.583–1.70, which is significantly higher than the discount rate of 0.02, thus highlighting its attractiveness as an investment.

This paper presents a non-destructive capacitance-based technique for rebars detection in reinforced concrete structure. The proposed technique depends on capacitance variation between the two electrodes of a co-planar capacitive sensor while scanning across different concrete sections with and without reinforcement bars. A mathematical model is used to provide meaningful interpretation for the effect of different sensor and concrete parameters on the measured capacitance. The finite element model is built and accompanied by a dedicated experimental setup to confirm theoretical estimations. Simulation and experimental results confirmed a detectable capacitance variation while scanning across the reinforced concrete slab in experimental setup for rebars detection.

This paper presents a study on the design and implementation of a real-time Internet of Things (IoT) framework for monitoring road conditions. The proposed system incorporates an accelerometer sensor attached to the rear axle of a vehicle to capture vertical acceleration signals resulting from the interaction between the vehicle suspension system and the road surface. These acceleration signals are utilized to determine vertical displacements, which are then used to calculate the International Roughness Index (IRI) as an indicator of road quality. The system comprises a hardware node for acquiring acceleration signals and a remote monitoring system for calculating and visually representing the IRI on a road map. Our results demonstrate that the proposed system is effective in automatically assessing road conditions, with an error in the calculated IRI of less than 3.5%